Bernhard Sick

4.8k total citations · 1 hit paper
219 papers, 3.0k citations indexed

About

Bernhard Sick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Bernhard Sick has authored 219 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Artificial Intelligence, 39 papers in Computer Vision and Pattern Recognition and 31 papers in Computer Networks and Communications. Recurrent topics in Bernhard Sick's work include Anomaly Detection Techniques and Applications (34 papers), Neural Networks and Applications (30 papers) and Autonomous Vehicle Technology and Safety (22 papers). Bernhard Sick is often cited by papers focused on Anomaly Detection Techniques and Applications (34 papers), Neural Networks and Applications (30 papers) and Autonomous Vehicle Technology and Safety (22 papers). Bernhard Sick collaborates with scholars based in Germany, United States and Finland. Bernhard Sick's co-authors include André Gensler, Janosch Henze, Nils Raabe, Thiemo Gruber, Sven Tomforde, Christian Gruber, Konrad Doll, Adrian Calma, Maciej Klimek and Jürgen Dickmann and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Bernhard Sick

206 papers receiving 2.9k citations

Hit Papers

Deep Learning for solar power forecasting — An approach u... 2016 2026 2019 2022 2016 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
    2016 425 citations André Gensler, Bernhard Sick et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Bernhard Sick Germany 25 1.4k 796 529 508 431 219 3.0k
Yimin Yang China 30 1.0k 0.8× 458 0.6× 870 1.6× 414 0.8× 220 0.5× 149 3.1k
Pavel Trojovský Czechia 29 1.9k 1.4× 992 1.2× 576 1.1× 213 0.4× 431 1.0× 119 4.4k
Mohammed A. Awadallah Jordan 40 2.3k 1.7× 1.1k 1.4× 518 1.0× 244 0.5× 514 1.2× 130 4.8k
Yong Guan China 29 676 0.5× 922 1.2× 631 1.2× 538 1.1× 615 1.4× 211 3.8k
Bo Li China 27 1.3k 1.0× 728 0.9× 771 1.5× 168 0.3× 1.2k 2.7× 224 3.8k
Otman Basir Canada 26 686 0.5× 690 0.9× 603 1.1× 160 0.3× 503 1.2× 183 2.7k
Ying Gao China 32 1.1k 0.8× 454 0.6× 462 0.9× 327 0.6× 775 1.8× 205 3.9k
Alaa Khamis Canada 21 948 0.7× 301 0.4× 731 1.4× 337 0.7× 757 1.8× 89 3.1k
Fatma A. Hashim Egypt 24 2.8k 2.1× 1.1k 1.4× 636 1.2× 205 0.4× 478 1.1× 82 5.2k
Yu Zhang China 31 2.0k 1.4× 395 0.5× 447 0.8× 965 1.9× 308 0.7× 212 4.9k

Countries citing papers authored by Bernhard Sick

Since Specialization
Citations

This map shows the geographic impact of Bernhard Sick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Bernhard Sick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bernhard Sick more than expected).

Fields of papers citing papers by Bernhard Sick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bernhard Sick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Bernhard Sick. The network helps show where Bernhard Sick may publish in the future.

Co-authorship network of co-authors of Bernhard Sick

This figure shows the co-authorship network connecting the top 25 collaborators of Bernhard Sick. A scholar is included among the top collaborators of Bernhard Sick based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Bernhard Sick. Bernhard Sick is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sick, Bernhard, et al.. (2024). Corner cases in machine learning processes. 6(1). 2 indexed citations
2.
Braun, Martin, Philipp Härtel, Christoph Scholz, et al.. (2024). Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1–5. 3 indexed citations
3.
Yang, Xiaohui, et al.. (2024). Casimir Effect in MEMS: Materials, Geometries, and Metrologies—A Review. Materials. 17(14). 3393–3393. 2 indexed citations
4.
Wegener, Thomas, et al.. (2023). On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision‐Based Tool. Advanced Engineering Materials. 25(21). 1 indexed citations
5.
Sick, Bernhard, et al.. (2023). Dataset of a parameterized U-bend flow for deep learning applications. Data in Brief. 50. 109477–109477. 2 indexed citations
6.
7.
Sick, Bernhard, et al.. (2023). Self- Integration and Agent Compatibility. 71–73.
8.
Vogt, S., et al.. (2023). PrOuD: Probabilistic Outlier Detection Solution for Time-Series Analysis of Real-World Photovoltaic Inverters. Energies. 17(1). 64–64. 1 indexed citations
9.
Hans, Andreas, Gregor Hartmann, Jens Viefhaus, et al.. (2022). Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses. Scientific Reports. 12(1). 17809–17809. 7 indexed citations
10.
Sajadifar, Seyed Vahid, et al.. (2022). Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods. Crystals. 12(9). 1281–1281. 12 indexed citations
11.
Sick, Bernhard, et al.. (2022). Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts. Energies. 15(21). 8062–8062. 6 indexed citations
12.
Doll, Konrad, et al.. (2022). Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories. IEEE Transactions on Intelligent Vehicles. 8(3). 2592–2603. 4 indexed citations
13.
Sick, Bernhard, et al.. (2021). CLeaR: An adaptive continual learning framework for regression tasks. arXiv (Cornell University). 3(1). 26 indexed citations
14.
15.
Calma, Adrian, et al.. (2018). Active Learning With Realistic Data - A Case Study. 1–8. 3 indexed citations
16.
Jänicke, Martin, Sven Tomforde, & Bernhard Sick. (2016). Towards Self-Improving Activity Recognition Systems Based on Probabilistic, Generative Models. 285–291. 13 indexed citations
17.
Gensler, André & Bernhard Sick. (2014). Novel Criteria to Measure Performance of Time Series Segmentation Techniques. LWA. 193–204. 11 indexed citations
18.
Tomforde, Sven, Jörg Hähner, Hella Seebach, et al.. (2014). Engineering and Mastering Interwoven Systems. OPUS (Augsburg University). 1–8. 25 indexed citations
19.
Sick, Bernhard, et al.. (2006). Architecture of computing systems - ARCS 2006 : 19th International Conference, Frankfurt/Main, Germany, March 13-16, 2006 : proceedings. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 1 indexed citations
20.
Gruber, Christian, et al.. (2006). Signature Verification with Dynamic RBF Networks and Time Series Motifs. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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